signatory
Differentiable computations of the signature and logsignature transforms, on both CPU and GPU. (ICLR 2021) (by patrick-kidger)
learning_with_signatures
Learning with Signatures (by decurtoydiaz)
signatory | learning_with_signatures | |
---|---|---|
1 | 2 | |
250 | 55 | |
- | - | |
0.0 | 0.0 | |
4 months ago | almost 2 years ago | |
C++ | HTML | |
Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
signatory
Posts with mentions or reviews of signatory.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-19.
-
[R] Authors Claim to Have "Solved" MNIST and CIFAR
As it happens, I know quite a lot about signatures. I spent half my PhD working on them. For example I am the author of the most popular library for computing signatures, which involved coming up with some new asymptotically optimal algorithms for computing them. So that's my credentials out the way.
learning_with_signatures
Posts with mentions or reviews of learning_with_signatures.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-19.
-
[R] Authors Claim to Have "Solved" MNIST and CIFAR
More details here.
What are some alternatives?
When comparing signatory and learning_with_signatures you can also consider the following projects:
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
serving - A flexible, high-performance serving system for machine learning models
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
tensorflow - An Open Source Machine Learning Framework for Everyone
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)